IchinoFS.SDA: Ichino's feature selection method for symbolic data

View source: R/IchinoFS.SDA.r

IchinoFS.SDAR Documentation

Ichino's feature selection method for symbolic data

Description

Ichino's method for identifiyng non-noisy variables in symbolic data set

Usage

IchinoFS.SDA(table.Symbolic)

Arguments

table.Symbolic

symbolic data table

Details

See file ../doc/IchinoFSSDA_details.pdf for further details

Value

plot

plot of the gradient illustrating combinations of variables, in which the axis of ordinates (Y) represents the maximum number of mutual neighbor pairs and the axis of the abscissae (X) corresponds to the number of features (m)

combination

the best combination of variables, i.e. the combination most differentiating the set of objects

maximum results

step-by-step combinations of variables up to m variables

calculation results

..............

Author(s)

Andrzej Dudek andrzej.dudek@ue.wroc.pl, Justyna Wilk justyna.wilk@ue.wroc.pl Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland http://keii.ue.wroc.pl/symbolicDA/

References

Ichino, M. (1994), Feature selection for symbolic data classification, In: E. Diday, Y. Lechevallier, P.B. Schader, B. Burtschy (Eds.), New Approaches in Classification and data analysis, Springer-Verlag, pp. 423-429.

Bock, H.H., Diday, E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.

Diday, E., Noirhomme-Fraiture, M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.

See Also

HINoV.SDA; HINoV.Symbolic in clusterSim library

Examples

# LONG RUNNING - UNCOMMENT TO RUN
#data("cars",package="symbolicDA")
#sdt<-cars
#ichino<-IchinoFS.SDA(sdt) 
#print(ichino) 

symbolicDA documentation built on May 28, 2022, 1:08 a.m.